Algorithmic Trading Using Python

Algorithmic trading (algo trading) refers to using automated trading strategies to execute buy/sell orders based on predefined conditions. Python is widely used in this field due to its rich ecosystem of libraries like Pandas, NumPy, Matplotlib, yfinance, TA-Lib, and Backtrader.

Key Concepts Covered:
✔ Fetching market data
✔ Implementing trading strategies
✔ Backtesting trading algorithms
✔ Risk management techniques

1. Installing Required Libraries

Ensure you have the necessary libraries installed before proceeding.

pip install pandas numpy matplotlib yfinance backtrader ta-lib

2. Fetching Market Data

We use yfinance to fetch stock data.

import pandas as pd
import yfinance as yf

# Fetch Apple (AAPL) stock data
df = yf.download("AAPL", start="2023-01-01", end="2024-01-01")

# Display the first 5 rows
print(df.head())

✔ Data Includes: Open, High, Low, Close, Adjusted Close, and Volume

3. Implementing a Simple Moving Average (SMA) Strategy

One of the simplest algo trading strategies is SMA Crossover:
Buy when the short-term SMA crosses above the long-term SMA
Sell when the short-term SMA crosses below the long-term SMA

# Calculate 50-day and 200-day SMA
df['SMA_50'] = df['Close'].rolling(window=50).mean()
df['SMA_200'] = df['Close'].rolling(window=200).mean()

# Define buy/sell signals
df['Signal'] = 0  
df.loc[df['SMA_50'] > df['SMA_200'], 'Signal'] = 1  # Buy signal
df.loc[df['SMA_50'] < df['SMA_200'], 'Signal'] = -1  # Sell signal

✔ Works well in trending markets but can fail in sideways markets.

4. Visualizing the Trading Signals

Using Matplotlib to plot buy/sell signals on a stock chart.

import matplotlib.pyplot as plt

plt.figure(figsize=(12,6))
plt.plot(df.index, df['Close'], label='Closing Price', color='blue')
plt.plot(df.index, df['SMA_50'], label='50-day SMA', color='green')
plt.plot(df.index, df['SMA_200'], label='200-day SMA', color='red')

# Mark Buy and Sell points
plt.scatter(df.index[df['Signal'] == 1], df['Close'][df['Signal'] == 1], label='Buy', marker='^', color='green')
plt.scatter(df.index[df['Signal'] == -1], df['Close'][df['Signal'] == -1], label='Sell', marker='v', color='red')

plt.legend()
plt.title("SMA Crossover Strategy")
plt.show()

✔ Helps visualize strategy performance

5. Backtesting Trading Strategies

📍 Backtesting evaluates the strategy’s historical performance.

import backtrader as bt

class SMAStrategy(bt.Strategy):
    def __init__(self):
        self.sma50 = bt.indicators.SimpleMovingAverage(self.data.close, period=50)
        self.sma200 = bt.indicators.SimpleMovingAverage(self.data.close, period=200)

    def next(self):
        if self.sma50[0] > self.sma200[0] and not self.position:
            self.buy()
        elif self.sma50[0] < self.sma200[0] and self.position:
            self.sell()

# Initialize backtest engine
cerebro = bt.Cerebro()
data = bt.feeds.PandasData(dataname=df)
cerebro.adddata(data)
cerebro.addstrategy(SMAStrategy)
cerebro.run()
cerebro.plot()

✔ Ensures a strategy is profitable before real trading

6. Risk Management & Stop Loss

Using ATR (Average True Range) to set a dynamic stop-loss.

df['ATR'] = df['High'].rolling(window=14).std()

# Define stop-loss level as 2x ATR below the entry price
df['Stop_Loss'] = df['Close'] - (2 * df['ATR'])

✔ Protects capital from extreme market movements

7. Automating Trade Execution

We use CCXT (CryptoCurrency eXchange Trading Library) for live execution on Binance, Coinbase, etc.

pip install ccxt

import ccxt

exchange = ccxt.binance()
symbol = 'BTC/USDT'

# Fetch order book data
order_book = exchange.fetch_order_book(symbol)
print(order_book)

✔ Execute real-time trades on cryptocurrency exchanges

8. Deploying a Live Trading Bot

To execute trades automatically, we integrate with a broker’s API.

# Place a market buy order (Example)
order = exchange.create_market_buy_order(symbol, amount=0.01)
print(order)

Steps to Deploy:
1️⃣ Develop and test strategies in a simulated environment
2️⃣ Use paper trading accounts before live trading
3️⃣ Implement fail-safe mechanisms to prevent losses